Mirroring computer vision and image classification methods, multiples studies such as [1]}, [2]}, [3]}, [4]} aim at deriving aerodynamic coefficients, predicting flow fields or airfoil shapes using convolutional neural networks (CNN) in a supervised manner. These studies take advantage of the NN's interpolation ability, optimising the bias-variance trade-off and surpassing the performances of most response-surface-based methods such as kriging. This enables faster design cycles, relying on these intermediate fidelity models. Linear decomposition methods such as Proper Orthogonal Decomposition (POD) or Dynamic Mode Decomposition (DMD) and their variations are increasingly challenged by non-linear reduced order modelling tools that demonstrate their capacity to compress state information further without losing in precision [5]}, [6]}, [7]}, [8]}, [9]}. Some studies, such as [10]}, even integrate a reduced order dynamical model, where pre-defined constraints (such as linearity and sparsity) help understand full-state dynamics. Recurrent structures such as long short-term memory cells (LSTM) provide a convenient way to integrate model dynamics [11]}, [12]}. Others leverage generative capacities of generative adversarial networks (GAN) and (variational) auto-encoders (VAE) to generate boundary layer velocity profiles, realistic homogeneous turbulence for direct numerical simulations (DNS) [13]}, [14]} or conversely to model turbulence [15]}. Yet these neural structures display poor extrapolation capabilities. Some authors try to mitigate this issue by embedding some key characteristics of fluid mechanics such as mass conservation or invariances in the models [16]}, [17]}, [18]}.
